Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Radical Formation: Abstraction00:47

Radical Formation: Abstraction

The electron of an atom can be abstracted from a compound by a relatively unstable radical to generate a new radical of relatively greater stability. For example, an initiator which forms radicals by homolysis can abstract a suitable species like a hydrogen atom or a halogen atom from a compound to generate a new radical. This ability of radicals to propagate by abstraction is a crucial feature of radical chain reactions.
Even though homolysis produces radicals, it is different from radical...
Concepts and Prototypes01:24

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Revisionist Views of Adolescent and Adult Cognition01:24

Revisionist Views of Adolescent and Adult Cognition

A revisionist approach to Jean Piaget's theory of cognitive development has brought new insights that challenge and reinterpret his established ideas. Piaget proposed that the formal operational stage, emerging in adolescence, represents the culmination of cognitive maturity. During this stage, individuals are said to develop abstract thinking, engage in systematic problem-solving, and show a form of egocentrism, believing others are as preoccupied with their behavior as they are themselves.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Latent subdimensions of anxiety and depression differentially influence exertion of effort in pursuit of reward versus avoidance of threat.

Translational psychiatry·2026
Same author

Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum.

eLife·2026
Same author

Octopamine and tyramine dynamics predict learning rate phenotypes during associative conditioning in honey bees.

Science advances·2026
Same author

Composing egocentric and allocentric maps for flexible navigation.

PLoS computational biology·2026
Same author

Infinite hidden Markov models can dissect the complexities of learning.

Nature neuroscience·2025
Same author

Individual differences in tail risk sensitive exploration using Bayes-adaptive Markov decision processes.

eLife·2025
Same journal

Are language models models?

The Behavioral and brain sciences·2026
Same journal

Large language models illuminate the mechanistic underpinnings of the creative aspect of language use (CALU), long regarded as a mystery.

The Behavioral and brain sciences·2026
Same journal

LLMs as a platform for studying constraint interaction: Motivation and challenges.

The Behavioral and brain sciences·2026
Same journal

Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods.

The Behavioral and brain sciences·2026
Same journal

Not-so-strange love: Language models and generative linguistic theories are more compatible than they appear.

The Behavioral and brain sciences·2026
Same journal

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

The Behavioral and brain sciences·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.8K

Representation, abstraction, and simple-minded sophisticates.

Peter Dayan1

  • 1Max-Planck-Gesellschaft, Max Planck-Ring 8, 72076Tübingen, Germany. dayan@tue.mpg.de https://www.kyb.tuebingen.mpg.de/publication-search/60427?person=persons217460.

The Behavioral and Brain Sciences
|July 11, 2020
PubMed
Summary
This summary is machine-generated.

Bayesian decision theory formally explains how representation and abstraction leverage prediction and predictive coding. This involves both model-free and model-based approaches in cognitive science.

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.5K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.6K

Related Experiment Videos

Last Updated: Jun 24, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.8K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.5K
Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.6K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Theory

Background:

  • Representation and abstraction are key in cognitive processes.
  • Prediction and predictive coding are fundamental mechanisms for information processing.
  • Understanding the interplay between these concepts is crucial for cognitive modeling.

Purpose of the Study:

  • To formally elucidate the role of representation and abstraction in prediction and predictive coding.
  • To explore the involvement of both model-free and model-based methods in these processes.
  • To provide a Bayesian decision-theoretic framework for understanding these cognitive mechanisms.

Main Methods:

  • Formal analysis using Bayesian decision theory.
  • Conceptual integration of representation, abstraction, prediction, and predictive coding.
  • Consideration of both model-free and model-based computational approaches.

Main Results:

  • Bayesian decision theory offers a unified framework for understanding representation and abstraction in predictive processes.
  • The framework highlights how representational choices influence predictive coding efficiency.
  • Both model-free and model-based strategies are shown to be integral to these Bayesian computations.

Conclusions:

  • Representation and abstraction are not merely passive elements but active components exploited by predictive coding.
  • Bayesian decision theory provides a powerful lens for dissecting the computational underpinnings of cognitive functions.
  • Future research can extend this framework to more complex cognitive tasks and neural implementations.